On-line Particle Size Research Articles

Development of an advanced separation and characterization platform for mRNA and lipid nanoparticles using multi-detector asymmetrical flow field-flow fractionation.

In recent years, the use of lipid nanoparticles (LNPs) for delivery of messenger RNA (mRNA)-based therapies has gained substantial attention in the field of drug development. In such an application, multiple LNP attributes have to be carefully characterized to ensure product safety and quality, whereas accurate and efficient characterization of these complex mRNA-LNP formulations remains a challenging endeavor. Here, we present the development and application of an online separation and characterization platform designed for the isolation and in-depth analysis of mRNAs and mRNA-loaded LNPs. Our asymmetrical flow field-flow fractionation with a multi-detector (MD-AF4) method has demonstrated exceptional resolution between mRNA-LNPs and mRNAs, delivering excellent recoveries (over 70%) for both analytes and exceptional repeatability. Notably, this platform allows for comprehensive and multi-attribute LNP characterization, including online particle sizing, morphology characterization, and determination of encapsulation efficiency, all within a single injection. Furthermore, real-time online sizing by synchronizing multi-angle light scattering (MALS) and dynamic light scattering (DLS) presented higher resolution over traditional batch-mode DLS, particularly in differentiating heterogeneous samples with a low abundance of large-sized particles. Additionally, our method proves to be a valuable tool for monitoring LNP stability under varying stress conditions. Our work introduces a robust and versatile analytical platform using MD-AF4 that not only efficiently provides multi-attribute characterizations of mRNA-LNPs but also holds promise in advancing studies related to formulation screening, quality control, and stability assessment in the evolving field of nanoparticle delivery systems for mRNAs.

小麦制粉粒度检测装置供料部件设计与研究

To address the issues of poor timeliness and delayed feedback in traditional wheat milling processes for manual particle size detection, a wheat milling online particle size detection device has been designed. This paper focuses on the design optimization of the key feeding component in the device, which affects the accuracy of particle size detection. The feeding component adopts shaftless screw blades. Through theoretical analysis, the main parameter ranges affecting the throughput capacity of the shaftless screw conveyor are determined. A Box-Behnken experiment is designed to obtain the optimal parameter combination for each factor: outer diameter of screw blades 23.8 mm, inner diameter of screw blades 6.4 mm, pitch 11.2 mm, and blade rotation speed 288.9 r/min. Simulation and test stand experiments are conducted using the above parameter combination. The simulation results show that the average throughput capacity of the feeding component is 2.85 kg/h, while the average throughput capacity of the test stand experiment is 2.84 kg/h, with a coefficient of variation of uniformity of 1.33%. These results indicate that the above parameter combination meets the design requirements for the feeding component in the device.

Online particle size analysis on conveyor belts with dense convolutional neural networks

Particle size distributions in ore feed systems, as well as the identification of particles in these feed systems can provide important information in the advanced control of unit operations in mineral processing, such as crushing and grinding circuits. Image analysis has long been considered a promising approach to achieve this, as it is an inexpensive, unobtrusive means of acquiring information rich measurements. It typically requires segmentation of images in order to identify individual particles. This is a challenging task to accomplish reliably, as variable lighting, fines adhering to larger particles or contiguous particles, as well as variable particle sizes and shapes can all compromise the accuracy of traditional algorithms. Image segmentation with deep learning methods have recently been investigated to surmount these difficulties. In this investigation, U-net and U-net with superpixel preprocessing with simple linear iterative clustering (SLIC) are proposed and compared with a traditional watershed algorithm. The U-net approaches were markedly more reliable than the watershed algorithm. In addition, preprocessing of the images with SLIC resulted in further improvement of the results.

Explaining Convolutional Neural Network Predictions of Particle Size in the Underflow of a Hydrocyclone

Hydrocyclones are classifiers that are widely used in closed-circuit grinding operations and they play a pivotal role in the production and energy efficiency of grinding circuits. Online performance monitoring of these units is not yet established, resulting in mostly manual control based on visual inspection by process operators. Previous studies have indicated that computer vision systems show promise for online particle size estimation using convolutional neural networks. In this paper, further validation of these results is provided by conducting explainability analyses of model predictions. A model was trained using GoogLeNet in transfer learning mode to classify the particle size distribution of the underflow of a pilot-scale hydrocyclone, using images of the underflow. The model could reliably separate the images into distinct classes. Occlusion sensitivity and Grad-CAM maps were used to visualize the internal reasoning of the models. These results indicated that the convolutional neural network was making predictions based on sensible characteristics of the underflow, such as the spray profile and slurry textures.

Dry process for SO2 and NOx removal via gas-to-particle conversion with ozone and ammonia injection

Because of the stability of its production and cost-effectiveness, coal is expected to be a primary energy source in near future. However, SO2 and NOx emissions from the coal combustion have been considered a serious environmental issue in the last few decades; they react with water vapor and ammonia (NH3) in the atmosphere to form secondary aerosols. In this study, ozone (O3) was injected to oxidize NO into NO2. Thereafter, NH3 was injected to convert SO2 into (NH4)2SO3 particles, which further reacted with NO2 to produce N2 and (NH4)2SO4 particles. These reaction mechanisms were discussed employing different chemical analyses of the products. Moreover, the size of the product particles was analyzed by online particle size analysis and the scanning electron microscopy images. Further, the quantitative analysis of the product was performed by ion chromatography, and the atomic balance of sulfur was verified by gas analysis. A higher SO2 concentration increased the NOx removal efficiency because the product ((NH4)2SO3), which was obtained from SO2 removal, acted as a reactant for the removal of NOx. Therefore, a higher concentration of O3 favored the improvement of the NOx removal efficiency since O3 oxidizes NO into NO2, which reacts with (NH4)2SO3. Thus, the SO2 and NOx removal efficiencies were 93.6% and 69.6%, respectively, at SO2, NO, and O3 concentrations of 900, 30, and 30 ppm, respectively.

Differential response of human lung epithelial cells to particulate matter in fresh and photochemically aged biomass-burning smoke

The chemical composition of particulate matter (PM) in biomass-burning smoke evolves upon aging in the atmosphere. The effect of this evolution on the toxicity of biomass-burning PM is understudied. Here, we burned oak foliage, pine needles, and hickory twigs in an environmental chamber. We used UV radiation to initiate photochemical aging of the emissions leading to the production of secondary organic aerosol (SOA), quantified using online particle size distribution measurements, and an overall increase in the PM oxygenation and decrease in the relative abundance of aromatic and condensed aromatic structures, obtained using ultra-high-resolution electrospray ionization mass spectrometry. In vitro exposure of human lung epithelial cells to PM from hickory combustion led to the strongest reduction in metabolic activity, followed by pine and oak, which was associated with the heavy metal content of the PM from the three fuels, quantified using induction-coupled plasma mass spectrometry. Furthermore, exposure to the fresh PM led to more reduction in metabolic activity than the aged PM for all fuels, whereas the aged PM induced more cell death by apoptosis. The differential cellular response to the fresh and aged PM indicates that the increase in oxygenation and decrease in aromaticity associated with photochemical aging alters the toxicity mechanisms exhibited by the PM, with a possible role of decreasing the heavy metal content (gram-metals per gram-PM) due to SOA formation. Together, these findings highlight the complex effect of photochemical aging on biomass-burning PM toxicity and motivate further studies to elucidate the underlying differences in toxicity mechanisms between fresh and aged PM.

Investigations into the sensing mechanism of acoustic emission sensors for particle size measurement in a particular case: normal incidence

In situ continuous particle size measurement is desirable in a variety of industries. Acoustic emission (AE) is a particularly suitable technique to achieve on-line continuous sizing of particles in pneumatic conveying pipelines, which utilizes the AE signals due to the impact of particles with a waveguide protruding into the particle flow. Although early attempts have been made to reveal the relationship between the AE parameters and the particle size, the fundamental sensing mechanism of the AE-based technique for particle size measurement is still not established. For instance, the effect of particle size distribution on several AE parameters remains to be examined. This article aims to gain an in-depth understanding of the AE sensing mechanism for on-line particle sizing by quantifying the parameters of the AE signal, including peak amplitude, count, rise time, duration, energy and root-mean-square value. A theoretical model considering the energy dissipation during plastic impact is also developed to determine the particle size from the AE signal. The proposed method is verified through experimental tests with glass beads on a single-particle test rig. The experimental results obtained indicate that the proposed method is feasible to infer particle size information from the energy of an impact event. The particle size can be measured with a relative error mostly within over a range from 0.4 mm to 1.2 mm.

Electrostatic sensors – Their principles and applications

Over the past three decades electrostatic sensors have been proposed, developed and utilised for the continuous monitoring and measurement of a range of industrial processes, mechanical systems and clinical environments. Electrostatic sensors enjoy simplicity in structure, cost-effectiveness and suitability for a wide range of installation conditions. They either provide unique solutions to some measurement challenges or offer more cost-effective options to the more established sensors such as those based on acoustic, capacitive, optical and electromagnetic principles. The established or potential applications of electrostatic sensors appear wide ranging, but the underlining sensing principle and resultant system characteristics are very similar. This paper presents a comprehensive review of the electrostatic sensors and sensing systems that have been developed for the measurement and monitoring of a range of process variables and conditions. These include the flow measurement of pneumatically conveyed solids, measurement of particulate emissions, monitoring of fluidised beds, on-line particle sizing, burner flame monitoring, speed and radial vibration measurement of mechanical systems, and condition monitoring of power transmission belts, mechanical wear, and human activities. The fundamental sensing principles together with the advantages and limitations of electrostatic sensors for a given area of applications are also introduced. The technology readiness level for each area of applications is identified and commented. Trends and future development of electrostatic sensors, their signal conditioning electronics, signal processing methods as well as possible new applications are also discussed.

Classification of stages of wear in spur gears based on wear debris morphology

This work investigates the morphological changes in the wear debris, generated during the different stages of gear wear. Wear debris are generated at the mating load-bearing tooth surfaces having relative motion. The number and size of collected wear debris provide useful information for the gear fault diagnosis.
 In the present work, both online and offline analyses of wear debris are carried out for gear fault diagnosis. In the online analysis, the oil from the gear sump is passed through online wear debris counter (to estimate the number of wear particles per minute) and particle size bin. Along with the online process, the oil samples are collected periodically, and wear debris particle images are captured using a scanning electron microscope (SEM). These images are subsequently processed to determine the parameters related to the shape, size and boundary features of the particles. The results of the modified texture in different stages of gear wear are reported. The average wear mass calculated as the actual area of the wear particles is combined with particle per minute is used as the stage’s classification.
 The combined online and offline study provides a better prediction of mild wear progression along with the information on the wear mechanisms at different stages of wear. The presence of different type of particles (ferrous and non-ferrous) points to degradation of specific components.

Particle and Gaseous Emissions from a Heavy-Duty SI Gas Engine over WHTC Driving Cycles

<div class="section abstract"><div class="htmlview paragraph">The use of gaseous fuels in internal combustion engines is increasing, due to several reasons, first of all their low environmental impact, large availability and low cost. Nevertheless, the need to reduce emissions also from gas engines is an important aspect to be considered in order to comply with future engine emissions regulations.</div><div class="htmlview paragraph">In this scenario, an extensive experimental activity was performed to fully characterize an heavy duty spark ignition engine, under development for Euro VI compliance and designed to run with gaseous fuels. Two separate sets of experiments were carried out, in order to analyze the engine behavior when burning LPG and CNG, respectively. To this aim, the engine was installed on a dynamic test bench, accurately instrumented to characterize the combustion evolution, performance and exhaust pollutant emissions, along the World Harmonized Transient Cycle (WHTC), the new European driving homologation cycle.</div><div class="htmlview paragraph">The main part of the manuscript addresses the analysis of the exhaust particulate emissions, in terms of soot concentration, particle number (PN) and particle size distribution function (PSDF). More in detail, a photo-acoustic sensor and a fast particulate spectrometer were adopted for on-line soot, PN and particle size measurements, during the transient engine tests.</div><div class="htmlview paragraph">The results revealed that although the gaseous emissions were within homologation limits, soot and PN could represent an issue for this class of engines. The experiments allowed to highlight that most part of the particles are emitted during specific phases of the driving cycle and could be ascribed to the engine oil vapors combustion.</div><div class="htmlview paragraph">Moreover, the investigation, indicating which engine operating conditions displayed the highest contribution to particles emissions, may provide helpful insights to deal with such critical conditions.</div></div>

Feedback Control for the Size and Shape Evolution of Needle-like Crystals in Suspension. III. Wet Milling

Several operating and control strategies with the purpose of manipulating the size and shape of crystals exhibiting needle-like morphology are presented using an ex situ wet mill as the actuator. A model-based operating policy and different model-free controllers are discussed. These controllers were coupled with an online particle size and shape monitoring tool. First, the attainable region in the size and shape space for a wet milling operation was evaluated in a simulation framework using a multidimensional population balance model describing the breakage phenomenon. Second, the operating and control strategies were tested experimentally in a lab-scale setup using two different milling configurations and two different compounds, namely, β l-glutamic acid and γ d-mannitol. The goal of the experiments was to drive various seed populations of the two compounds to several target average lengths in the size and shape space. It was observed that the model-based operating policy, run without any feedback acti...

Estimation of online particle size distribution of a particle mixture in free fall with acoustic emission

For many powder processes, the particle size distribution (PSD) is a key quality attribute of the flow properties of the process powders. This paper presents a method for estimating the PSD with acoustic emissions (AE) by implementing a time domain-based threshold approach followed by the extraction of the amplitude mean from each set threshold and correlation to particle size. The experiments were carried out using a powder-free fall experimental rig, and a set of glass beads, while the acquired data were analyzed with a designed signal analysis method. The results of the experiments showed that the PSD of the particle mixtures being investigated could be identified with an average absolute error of 10%. The main advantage of the designed signal analysis method was identified as the requirement for a low hardware complexity due to a simpler algorithm than its predecessors. In an attempt to benchmark the performance of the system, the performance of the designed approach was compared to a wavelet-based analysis designed by Ren et al. From this it was seen that the approach used by Ren et al. is reliant on a tuning process to aid the algorithm in making the size estimation, suggesting that Ren’s approach would prove to be inefficient in a process where powder segregation occurred due to poor mixing.

Assessing the benefits of automatic grinding control using PST technology for true on-line Particle Size Measurement

Despite the recognized significance of ground product fineness on both line capacity and downstream process performance (typically flotation), less than 10% of mineral ore concentrators today use real-time particle size measurements in automatic control applications for the final product particle size. Although “near-line” particle size measurement instrumentation has been in place for several decades and is commonly installed in most modern concentrators, its availability and low measurement frequency have typically been inadequate for reliable use in automatic control. Their low availability is often associated with problems with slurry sample collection and handling systems required to present the samples to the size measurement instrument. Recently, an innovative technology for real-time, on-line particle size measurement has been developed by CiDRA Minerals Processing, under its commercial name Particle Size Tracking (PST), already proven in several commercial installations, demonstrating near 100% availability with minimal maintenance, thus overcoming the limitations of previous technologies. Investment decision makers require convincing, reliable estimates of the expected economic value that automatic grind control projects will deliver. This paper presents a methodology for estimating such value from the installation of the new PST particle size measurement technology, capable of tracking particle size on each individual hydrocyclone overflow stream, thus adding significant new options for improved process stability and performance. Based on actual plant operating records, accumulated over long periods of time, the evaluation approach herein described assesses and highlights the significant potential contribution to be expected from this unique PST technology.

Investigation of Fluidized Bed Granulation Mechanism by Using an Online Particle Size Monitoring Device

Investigation of Fluidized Bed Granulation Mechanism by Using an Online Particle Size Monitoring Device

Methodology for assessing the benefits of grind control using particle size tracking technology for true on-line particle size measurement

Methodology for assessing the benefits of grind control using particle size tracking technology for true on-line particle size measurement

Online particle size distribution estimation of a mixture of similar sized particles with acoustic emissions

Particle processing plants regard the Particle Size Distribution (PSD) as a key quality factor as it influences the bulk and flow properties of the particles. In this work, Acoustic Emission (AE) is used to estimate the PSD of a mixture that comprise of similar sized particles. The experiments involved the use of regular sized particles (glass beads) and with the aid of a time domain based threshold analysis of the particle impacts the PSD of the mixtures could be estimated.

Chemical Features of Particles Generated in an Ethylene/Ethanol Premixed Flame

An ethylene\ethanol premixed flame has been investigated to elucidate the effect of ethanol addition on particle morphology and chemical features in controlled flame conditions. In situ optical techniques and ex situ online particle size distribution measurements showed that soot formation is strongly reduced for the effect of ethanol addition to ethylene and that soot particle size is much lower with respect to the size of the soot particles formed in a neat ethylene flame operated under the same combustion conditions. It was also noticed that the formation of nanoparticles with sizes smaller than 10 nm is not significantly affected by ethanol addition to ethylene; their concentration remains practically unchanged or even increases for the effect of ethanol addition. Particles produced in the ethylene/ethanol flame showed a higher reactivity with respect to the ethylene-generated particles. Raman spectra indicated that the presence of ethanol inhibits the aromatization process, showing, on average, a smaller size of the aromatic island within the particles. Ultraviolet–visible analysis confirmed this finding, showing a marked molecular character for the particles collected in the ethylene/ethanol flame with respect to the neat ethylene flame, especially for the particles with sizes smaller than 20 nm. Finally, Fourier transform infrared spectra evidenced the presence of oxygen functionalities onto the particles formed when ethanol is used; the molecular character and bonded oxygen atoms can contribute to the major reactivity of the ethanol-generated particles, as also evidenced by thermogravimetric analysis.

Development and beyond: Strategy for long-term maintenance of an online laser diffraction particle size method in a spray drying manufacturing process

The purpose of this manuscript is to present the intended use and long-term maintenance strategy of an online laser diffraction particle size method used for process control in a spray drying process. A Malvern Insitec was used for online particle size measurements and a Malvern Mastersizer was used for offline particle size measurements. The two methods were developed in parallel with the Mastersizer serving as the reference method. Despite extensive method development across a range of particle sizes, the two instruments demonstrated different sensitivities to material and process changes over the product lifecycle. This paper will describe the procedure used to ensure consistent alignment of the two methods, thus allowing for continued use of online real-time laser diffraction as a surrogate for the offline system over the product lifecycle.

On-line measurement of the size distribution of particles in a gas–solid two-phase flow through acoustic sensing and advanced signal analysis

Acoustic emission (AE) technology is a promising approach to non-intrusively measure the size distribution of particles in a pneumatic suspension. This paper presents an experimental study of the AE sensing technology coupled with signal processing algorithms for on-line particle sizing. The frequency characteristics of the AE signals under different experimental conditions are studied and compared. Initially, the characteristics of the background noise and AE signals are compared in the frequency domain for different air velocities and particle feeding rates. Through short-term energy analysis the working features of the suction unit and the vibration feeder are revealed. To find the effective characteristic frequency band of the AE signals, a multiple scanning and accumulation method assisted with a Savitzky–Golay smoothing filter is used to denoise the power spectra of the signals. Wavelet analysis is also deployed to denoise the signals. The denoising performance of different wavelet parameters (wavelet function, decomposition level and thresholding) is compared in terms of signal-to-noise ratio and signal smoothness. Finally, particle size is predicted through a neural network with energy fraction extracted through wavelet analysis. Experimental results demonstrate that the relative error of the particle sizing system is no greater than 23%.

Holographic particle sizing and locating by using Hilbert-Huang transform.

By using the Hilbert-Huang transform, a novel method is proposed to perform the task of particle sizing and axial locating directly from in-line digital holograms rather than reconstructing the optical field. The intensity distribution of the particle hologram is decomposed into intrinsic mode functions (IMFs) by the empirical mode decomposition. From the Hilbert spectrum of these IMFs, the axial location of the particle can be calculated by fitting the spectrum to a straight line, and the particle size can be derived from the singularities of the spectrum. Our method does not need to predefine any basis function; thus the whole process is fast and efficient. The validity and accuracy of the method are demonstrated by the numerical simulations and experiments. It is expected that this method can be used in on-line particle sizing and 3D tracking.